Systemic lupus erythematosus (SLE or lupus) is a multi-system, clinically heterogeneous autoimmune disease with substantial genetic basis. SLE disproportionately affects women (90%) and ethnic minorities like African- Americans (AA). Compared to European-Americans (EA), AA show 3-5 fold higher prevalence and have more severe clinical manifestations and organ damage, especially kidneys (lupus nephritis). Genetic variation between ethnicities could account for underlying differences in disease severity and clinical manifestations. However, the genetic architecture of lupus, especially in AA, is largely unknown. While recent genome-wide association studies (GWAS) on European and Asian ancestries identified over 40 susceptibility loci, none of these were focused in AA to verify the robustness of these association or identify novel signals. Additionally, since the majority of associated variants are located in introns or intragenic regions, GWAS is not successful for pinpointing actual predisposing variants or providing the full allelic spectrum of causal variants underlying these association signals. Therefore, it is difficult to predict functional consequences of genetic association. This poor understanding of underlying biological mechanisms hinders improvements in the diagnosis and treatment for SLE. Our research team has acquired experience, expertise, resources, and infrastructure necessary to move beyond GWAS to accelerate the discovery and characterization of causal variants underlying GWAS signals. We have successfully identified functional SLE predisposing variants in ITGAM, IFIH1 and NCF2, and propose extending this discovery effort to other candidate genes in AA. This is an essential prerequisite to understanding disease disparities in SLE. Our experimental design incorporates data from genetics (including sequencing), clinical sub-phenotypes and autoantibodies, eQTLs, and ENCODE (annotation of enhancers, chromatin states, DNA and histone methylation, etc.), followed by bioinformatics and molecular modeling for understanding the mechanistic effects to predict functional SNPs. Aim 1 is to perform targeted deep-sequencing on >1500 AA samples to thoroughly assess 25 strongly associated (10-24<p<10-6) signals. Aim 2 is to conduct imputation-based association analysis using out-of-study controls (dbGaP) (>18,000) in order to maximize power to detect associated variants, and confirm these associations in >4000 AA samples. Our proposed cohort has adequate power to detect both rare and common variants. Aim 3 is to elucidate genetic and clinical heterogeneity of SLE by assessing association between predisposing variants and SLE clinical sub-phenotypes (e.g., lupus nephritis) and autoantibodies. Aim 4 is to predict mechanistic effects of SLE-predisposing variants using bioinformatics analysis and molecular modeling. Ultimately, this project will yield a set of SLE associated functional variants in AA, providing the basis for in-depth biological experiments to define the pathological mechanisms, and define genetic architecture to uncover underlying health disparities. This may define novel targets and guide options for future therapeutic interventions.